Genomic Data Standardization Across Molecular Profiling Labs
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Solution Overview
Problem
Current methods for analyzing genomic data from molecular profiling labs face challenges due to the lack of an industry-accepted standard for data structure, leading to inconsistent and unstandardized formatting, making it difficult to present data from different labs in a clinically consistent and scalable manner.
Innovation Solution
A system and method that standardize medical testing data by accessing records in different formats, determining associations with common value classifiers, and storing them in a database, allowing for the conversion and display of genomic data elements in a standardized format through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from different molecular profiling labs is collected and analyzed, then the quantity and diversity of genomic data increases, but the data becomes inconsistent and unstandardized across different sources
Solution Approach 1:
The patent creates a universal standardized data structure that can accommodate and represent genomic data from multiple different molecular profiling labs with varying formats. This universal structure serves as a common framework that can handle diverse input formats while maintaining consistency in the standardized output representation.
Solution Approach 2:
The patent transforms data from various formats by changing the parameters and structure of the data representation. It converts inconsistent data elements into a standardized format with consistent parameters, allowing for uniform analysis while preserving the underlying genomic information from different sources.
2Measurement precision
If manual extraction of information from genomic data is performed, then data accuracy can be maintained, but the time and resources required become infeasible for large datasets
Solution Approach 1:
The patent replaces the manual mechanical process of data extraction with an automated computational system. It uses computer-based methods with structured data formats and standardized schemas to automatically extract and process genomic data, eliminating the need for manual extraction while maintaining accuracy through systematic validation rules.
Solution Approach 2:
The patent performs preliminary actions by pre-defining standardized data structures, schemas, and validation rules before data analysis begins. This preparation enables automated systems to efficiently process and validate data without manual intervention, reducing time loss while maintaining extraction accuracy through pre-established standards.
3Productivity
If computer-based extraction methods are used to process large volumes of genomic data, then processing speed increases, but the methods become ineffective due to inconsistent formatting across labs
Solution Approach 1:
The patent changes the parameters of data representation by implementing a standardized format that transforms inconsistent raw data into uniform structured data. This parameter transformation enables computer-based methods to effectively process data from different sources by converting varied formats into a consistent structure that automated systems can reliably interpret.
Solution Approach 2:
The patent introduces an intermediary standardized data structure that acts as a mediator between diverse lab formats and computer-based processing systems. This intermediate representation layer translates various input formats into a universal structure, allowing automated extraction methods to maintain both speed and reliability when processing data from multiple sources.
4Ease of operation
If genomic data is presented in standardized format, then data accessibility and usability improve, but the complexity of standardization processes increases
Solution Approach 1:
The patent creates a universal standardized data structure that serves multiple functions: it can represent diverse genomic data types, accommodate different lab formats, enable automated processing, and improve data accessibility. This multi-functional design achieves ease of operation while managing complexity through a unified approach rather than multiple specialized systems.
Data Source
AI summary
A system for standardizing medical testing data may include a processor. The processor may be programmed to access a first medical testing record including a first data element represented in a first data format; access a second medical testing record including a second data element represented in a second data format, the second data format being different from the first data format; determine that the first data element and the second data element are associated with a common value classifier; and store the first data element and the second data element in a database in association with the common value classifier.